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# Mem0: The Memory Layer for Personalized AI Mem0 provides a smart, self-improving memory layer for Large Language Models, enabling personalized AI experiences across applications. > Note: The Mem0 repository now also includes the Embedchain project. We continue to maintain and support Embedchain ❀️. You can find the Embedchain codebase in the [embedchain](https://github.com/mem0ai/mem0/tree/main/embedchain) directory. ## πŸš€ Quick Start ### Installation ```bash pip install mem0ai ``` ### Basic Usage ```python import os from mem0 import Memory os.environ["OPENAI_API_KEY"] = "xxx" # Initialize Mem0 m = Memory() # Store a memory from any unstructured text result = m.add("I am working on improving my tennis skills. Suggest some online courses.", user_id="alice", metadata={"category": "hobbies"}) print(result) # Created memory: Improving her tennis skills. Looking for online suggestions. # Retrieve memories all_memories = m.get_all() print(all_memories) # Search memories related_memories = m.search(query="What are Alice's hobbies?", user_id="alice") print(related_memories) # Update a memory result = m.update(memory_id="m1", data="Likes to play tennis on weekends") print(result) # Get memory history history = m.history(memory_id="m1") print(history) ``` ## πŸ”‘ Core Features - **Multi-Level Memory**: User, Session, and AI Agent memory retention - **Adaptive Personalization**: Continuous improvement based on interactions - **Developer-Friendly API**: Simple integration into various applications - **Cross-Platform Consistency**: Uniform behavior across devices - **Managed Service**: Hassle-free hosted solution ## πŸ“– Documentation For detailed usage instructions and API reference, visit our documentation at [docs.mem0.ai](https://docs.mem0.ai). ## πŸ”§ Advanced Usage For production environments, you can use Qdrant as a vector store: ```python from mem0 import Memory config = { "vector_store": { "provider": "qdrant", "config": { "host": "localhost", "port": 6333, } }, } m = Memory.from_config(config) ``` ## πŸ—ΊοΈ Roadmap - Integration with various LLM providers - Support for LLM frameworks - Integration with AI Agents frameworks - Customizable memory creation/update rules - Hosted platform support ## πŸ™‹β€β™‚οΈ Support Join our Slack or Discord community for support and discussions. If you have any questions, feel free to reach out to us using one of the following methods: - [Join our Discord](https://embedchain.ai/discord) - [Join our Slack](https://embedchain.ai/slack) - [Follow us on Twitter](https://twitter.com/mem0ai) - [Email us](mailto:founders@mem0.ai)